Lectures related to Deep Generative Models in Drug Discovery

Generative Models: Advancements in Molecular Design

Explores the use of generative models for discovering novel molecules in molecular design.

Drug Discovery: Computational Approaches

Explores the EXSCALATE4COV project, focusing on computational drug discovery for COVID-19 treatments and the collaboration between academia and industry.

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Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Decentralized Optimization

Explores decentralized optimization in machine learning, emphasizing robustness, privacy, and fairness in collaborative learning.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Computational Aspects of Optimization

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

Fragment-Based Drug Discovery: Concepts & Technologies

Covers the concepts and technologies of fragment-based drug discovery, exploring hit identification, lead optimization, and screening methods.

Interrogating SAR in Medical Chemistry

Delves into the fundamentals of medicinal chemistry, emphasizing the interrogation of Structure-Activity Relationships (SAR) in drug design.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Automating Scanning Probe Microscopy: Techniques and Applications

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Deep and Robust Reinforcement Learning Techniques

Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.